Leveraging Artificial Intelligence to bridge execution gaps in SAFe®-Scaled Agile based Programs
Abstract
Large enterprises adopting the Scaled Agile Framework (SAFe®) often face challenges in Program Increment (PI) planning accuracy, objective backlog prioritization, timely risk detection, and manual compliance verification. We surveyed 12 teams across 10 Agile Release Trains (ARTs) to quantify these gaps. To address them, we propose an AI-augmented DevOps pipeline—built on common Infrastructure as Code tools—to integrate predictive analytics, natural language processing, reinforcement learning, and anomaly detection. Experimental results on enterprise projects show a 35 % reduction in PI-velocity forecasting error, 20 % faster backlog lead time, and 62.5 % quicker risk detection.
Fields of Study & Subjects
Software System Performance and Reliability
Software Engineering Research
Software Engineering Techniques and Practices
Publication details
- Journal / source
- World Journal of Advanced Engineering Technology and Sciences
- Publisher
- GSC Online Press
- Volume
- 18
- Issue
- 1
- Pages
- 001–006
- Publication date
- 6 January 2026